Massive Web Archive
Billions of web pages captured since 2008, with monthly updates and metadata-rich records for AI and analytics.
Common Crawl: The Open Web Archive for Large-Scale AI and Analytics
Free, large-scale web data repository for AI training, research, and analytics
Overview
Founded in 2007, Common Crawl is a free and open repository of global web data.
It provides billions of archived web pages in raw formats like WARC and metadata, making it a powerful resource for AI training, Retrieval-Augmented Generation (RAG), and historical research.
While unstructured and requiring preprocessing, its scale and openness make it invaluable for developers, researchers, and data scientists.
Main Features
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WARC and Metadata Access
Access raw HTML, WARC files, and metadata including timestamps, MIME types, status codes, and digests for reproducibility.
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Historical Snapshots
Retrieve time-based captures of websites for AI benchmarks, reproducibility, and research into the evolution of the web.
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Python Integration
Easily query and process data with Python using libraries like warcio for extracting and parsing WARC files.
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Community Resources
Leverage crawl overviews, webgraph statistics, AI agent support, and open-source tools built on Common Crawl.
Why Teams
Choose Common Crawl
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Free and Open
Access petabytes of web data without licensing or paywalls -
Massive Scale
Billions of pages spanning nearly two decades of internet history -
Metadata-Rich
Detailed fields like digest, timestamp, MIME type, and status codes for filtering and analysis -
AI-Friendly
Ideal for building training datasets, RAG pipelines, and reproducibility workflows
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Final Thoughts
Common Crawl is not a plug-and-play dataset, but a raw, large-scale archive ideal for AI training, reproducibility, and historical research. For teams ready to parse and clean the data, it unlocks nearly two decades of open web history — for free.
